Learn Kedro with hands-on video¶
If you like to learn from video, you can follow our hands-on course “Introduction to Kedro: Building Maintainable Data Pipelines” on YouTube.
The course is structured into sections and these are each broken into short videos that cover specific Kedro topics. You’ll walk through the spaceflights tutorial and get hands-on with the example. Along the way, you’ll learn key Kedro concepts like datasets and the Kedro Data Catalog, nodes and pipelines, and configuration management.
Who is this course for?¶
This course is for data scientists, data engineers and machine learning engineers. You can be junior, mid-level or senior in your field of work. You’re likely to be hands-on with projects, or a decision-maker who regularly makes design and implementation choices about Python data products.
We assume you know these concepts:
Python basics (coding on Jupyter and other notebook interfaces)
Manipulating data with pandas
Command line basics
We don’t assume knowledge of software engineering in Python, so the course contains information about reusability principles, how to create a Python package, and how to use version control.
What you’ll learn¶
In short, you’ll learn answers to the following:
Introduction to Kedro
What is Kedro? How does it help you create maintainable, reusable data science code?
How does Kedro fit into the data science ecosystem?
What do you need to do to create a Kedro project?
How can you refactor a Jupyter notebook to a Kedro project?
How do you package Python code as a library?
How do you work with Kedro projects in VSCode?
What are namespaces and dataset factories?
What is needed to deploy a Kedro project using container solutions like Docker and open source orchestrators like Airflow?
What are Kedro plugins?
How can you contribute to Kedro?
You don’t need to register for the course and you can skip around the sections to find help on a particular area as you pick up the skills needed to build your own Kedro projects.
Index of videos¶
Introduction to Kedro: Building Maintainable Data Pipelines is split into the following videos: